OpenRaiser/Intern-Atlas huggingface dataset:
Data Dropout: Optimizing Training Data for Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Deep learning models learn to fit training data while they are highly expected to generalize well to testing data. Most works aim at finding such models by creatively designing architectures and fine-tuning parameters. To adapt to particular tasks, hand-crafted information such as image prior has also been incorporated into end-to-end learning. However, very little progress has been made on investigating how an individual training sample will influence the generalization ability of a model. In other words, to achieve high generalization accuracy, do we really need all the samples in a training dataset? In this paper, we demonstrate that deep learning models such as convolutional neural networks may not favor all training samples, and generalization accuracy can be further improved by dropping those unfavorable samples. Specifically, the influence of removing a training sample is quantifiable, and we propose a Two-Round Training approach, aiming to achieve higher generalization accuracy. We locate unfavorable samples after the first round of training, and then retrain the model from scratch with the reduced training dataset in the second round. Since our approach is essentially different from fine-tuning or further training, the computational cost should not be a concern. Our extensive experimental results indicate that, with identical settings, the proposed approach can boost performance of the well-known networks on both high-level computer vision problems such as image classification, and low-level vision problems such as image denoising.
Post-mortem Human Iris Recognition
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents a unique analysis of post-mortem human iris recognition. Post-mortem human iris images were collected at the university mortuary in three sessions separated by approximately 11 hours, with the first session organized from 5 to 7 hours after demise. Analysis performed for four independent iris recognition methods shows that the common claim of the iris being useless for biometric identification soon after death is not entirely true. Since the pupil has a constant and neutral dilation after death (the so called "cadaveric position"), this makes the iris pattern perfectly visible from the standpoint of dilation. We found that more than 90% of irises are still correctly recognized when captured a few hours after death, and that serious iris deterioration begins approximately 22 hours later, since the recognition rate drops to a range of 13.3-73.3% (depending on the method used) when the cornea starts to be cloudy. There were only two failures to enroll (out of 104 images) observed for only a single method (out of four employed in this study). These findings show that the dynamics of post-mortem changes to the iris that are important for biometric identification are much more moderate than previously believed. To the best of our knowledge, this paper presents the first experimental study of how iris recognition works after death, and we hope that these preliminary findings will stimulate further research in this area.
Database of iris images acquired in the presence of ocular pathologies and assessment of iris recognition reliability for disease-affected eyes
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents a database of iris images collected from disease affected eyes and an analysis related to the influence of ocular diseases on iris recognition reliability. For that purpose we have collected a database of iris images acquired for 91 different eyes during routine ophthalmology visits. This collection gathers samples for healthy eyes as well as those with various eye pathologies, including cataract, acute glaucoma, posterior and anterior synechiae, retinal detachment, rubeosis iridis, corneal vascularization, corneal grafting, iris damage and atrophy and corneal ulcers, haze or opacities. To our best knowledge this is the first database of such kind that will be made publicly available. In the analysis the data were divided into five groups of samples presenting similar anticipated impact on iris recognition: 1) healthy (no impact), 2) unaffected, clear iris (although the illness was detected), 3) geometrically distorted irides, 4) distorted iris tissue and 5) obstructed iris tissue. Three different iris recognition methods (MIRLIN, VeriEye and OSIRIS) were then used to find differences in average genuine and impostor comparison scores calculated for healthy eyes and those impacted by a disease. Specifically, we obtained significantly worse genuine comparison scores for all iris matchers and all disease-affected eyes when compared to a group of healthy eyes, what have a high potential of impacting false non-match rate.
VoxSegNet: Volumetric CNNs for Semantic Part Segmentation of 3D Shapes
Venue: arXiv cs.CV 2018
Year: 2018

Voxel is an important format to represent geometric data, which has been widely used for 3D deep learning in shape analysis due to its generalization ability and regular data format. However, fine-grained tasks like part segmentation require detailed structural information, which increases voxel resolution and thus causes other issues such as the exhaustion of computational resources. In this paper, we propose a novel volumetric convolutional neural network, which could extract discriminative features encoding detailed information from voxelized 3D data under a limited resolution. To this purpose, a spatial dense extraction (SDE) module is designed to preserve the spatial resolution during the feature extraction procedure, alleviating the loss of detail caused by sub-sampling operations such as max-pooling. An attention feature aggregation (AFA) module is also introduced to adaptively select informative features from different abstraction scales, leading to segmentation with both semantic consistency and high accuracy of details. Experiment results on the large-scale dataset demonstrate the effectiveness of our method in 3D shape part segmentation.
Congestion Control for RTP Media: a Comparison on Simulated Environment
Venue: arXiv cs.NI 2018
Year: 2018

To develop low latency congestion control algorithm for real time taffic has been gained attention recently. RTP Media Congestion Avoidance Techniques (RMCAT) working group was initiated for standard defination. There are three algorithms under this group, Network Assisted Dynamic Adaptation (NADA) proposed by Cisco, Google Congestion Control (GCC) proposed by Google and Self-Clocked Rate Adaptation for Multimedia(SCReAM) proposed by Ericsson. This paper compares and analyses the performance of these algorithms on simulation environment. Results show GCC has well fairness property and performs well in lossy link but slow convergence in dynamic link, NADA stabilizes its rate quickly but suffers "late-comer" effect, SCReAM has the lowest queue occupation but also lower link capacity utilization.
A Datamining Approach for Emotions Extraction and Discovering Cricketers performance from Stadium to Sensex
Venue: arXiv cs.IR 2018
Year: 2018

Microblogging sites are the direct platform for the users to express their views. It has been observed from previous studies that people are viable to flaunt their emotions for events (eg. natural catastrophes, sports, academics etc.), for persons (actor/actress, sports person, scientist) and for the places they visit. In this study we focused on a sport event, particularly the cricket tournament and collected the emotions of the fans for their favorite players using their tweets. Further, we acquired the stock market performance of the brands which are either endorsing the players or sponsoring the match in the tournament. It has been observed that performance of the player triggers the users to flourish their emotions over social media therefore, we observed correlation between players performance and fans' emotions. Therefore, we found the direct connection between player's performance with brand's behavior on stock market.
Learning to Entangle Radio Resources in Vehicular Communications: An Oblivious Game-Theoretic Perspective
Venue: arXiv cs.NI 2018
Year: 2018

This paper studies the problem of non-cooperative radio resource scheduling in a vehicle-to-vehicle communication network. The technical challenges lie in high vehicle mobility and data traffic variations. Over the discrete scheduling slots, each vehicle user equipment (VUE)-pair competes with other VUE-pairs in the coverage of a road side unit (RSU) for the limited frequency to transmit queued packets. The frequency allocation at the beginning of each slot by the RSU is regulated following a sealed second-price auction. Each VUE-pair aims to optimize the expected long-term performance. Such interactions among VUE-pairs are modelled as a stochastic game with a semi-continuous global network state space. By defining a partitioned control policy, we transform the stochastic game into an equivalent game with a global queue state space of finite size. We adopt an oblivious equilibrium (OE) to approximate the Markov perfect equilibrium (MPE), which characterizes the optimal solution to the equivalent game. The OE solution is theoretically proven to be with an asymptotic Markov equilibrium property. Due to the lack of a priori knowledge of network dynamics, we derive an online algorithm to learn the OE policies. Numerical simulations validate the theoretical analysis and show the effectiveness of the proposed online learning algorithm.
Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Despite the remarkable progress in face recognition related technologies, reliably recognizing faces across ages still remains a big challenge. The appearance of a human face changes substantially over time, resulting in significant intra-class variations. As opposed to current techniques for age-invariant face recognition, which either directly extract age-invariant features for recognition, or first synthesize a face that matches target age before feature extraction, we argue that it is more desirable to perform both tasks jointly so that they can leverage each other. To this end, we propose a deep Age-Invariant Model (AIM) for face recognition in the wild with three distinct novelties. First, AIM presents a novel unified deep architecture jointly performing cross-age face synthesis and recognition in a mutual boosting way. Second, AIM achieves continuous face rejuvenation/aging with remarkable photorealistic and identity-preserving properties, avoiding the requirement of paired data and the true age of testing samples. Third, we develop effective and novel training strategies for end-to-end learning the whole deep architecture, which generates powerful age-invariant face representations explicitly disentangled from the age variation. Moreover, we propose a new large-scale Cross-Age Face Recognition (CAFR) benchmark dataset to facilitate existing efforts and push the frontiers of age-invariant face recognition research. Extensive experiments on both our CAFR and several other cross-age datasets (MORPH, CACD and FG-NET) demonstrate the superiority of the proposed AIM model over the state-of-the-arts. Benchmarking our model on one of the most popular unconstrained face recognition datasets IJB-C additionally verifies the promising generalizability of AIM in recognizing faces in the wild.
Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations
Venue: arXiv cs.CL 2018
Year: 2018

Recent works in neural machine translation have begun to explore document translation. However, translating online multi-speaker conversations is still an open problem. In this work, we propose the task of translating Bilingual Multi-Speaker Conversations, and explore neural architectures which exploit both source and target-side conversation histories for this task. To initiate an evaluation for this task, we introduce datasets extracted from Europarl v7 and OpenSubtitles2016. Our experiments on four language-pairs confirm the significance of leveraging conversation history, both in terms of BLEU and manual evaluation.
Natural Language Person Search Using Deep Reinforcement Learning
Venue: arXiv cs.CV 2018
Year: 2018

Recent success in deep reinforcement learning is having an agent learn how to play Go and beat the world champion without any prior knowledge of the game. In that task, the agent has to make a decision on what action to take based on the positions of the pieces. Person Search is recently explored using natural language based text description of images for video surveillance applications (S.Li et.al). We see (Fu.et al) provides an end to end approach for object-based retrieval using deep reinforcement learning without constraints placed on which objects are being detected. However, we believe for real-world applications such as person search defining specific constraints which identify a person as opposed to starting with a general object detection will have benefits in terms of performance and computational resources required. In our task, Deep reinforcement learning would localize the person in an image by reshaping the sizes of the bounding boxes. Deep Reinforcement learning with appropriate constraints would look only for the relevant person in the image as opposed to an unconstrained approach where each individual objects in the image are ranked. For person search, the agent is trying to form a tight bounding box around the person in the image who matches the description. The bounding box is initialized to the full image and at each time step, the agent makes a decision on how to change the current bounding box so that it has a tighter bound around the person based on the description of the person and the pixel values of the current bounding box. After the agent takes an action, it will be given a reward based on the Intersection over Union (IoU) of the current bounding box and the ground truth box. Once the agent believes that the bounding box is covering the person, it will indicate that the person is found.
PlutoAR: An Inexpensive, Interactive And Portable Augmented Reality Based Interpreter For K-10 Curriculum
Venue: arXiv cs.HC 2018
Year: 2018

The regular K-10 curriculums often do not get the necessary of affordable technology involving interactive ways of teaching the prescribed curriculum with effective analytical skill building. In this paper, we present "PlutoAR", a paper-based augmented reality interpreter which is scalable, affordable, portable and can be used as a platform for skill building for the kids. PlutoAR manages to overcome the conventional albeit non-interactive ways of teaching by incorporating augmented reality (AR) through an interactive toolkit to provide students with the best of both worlds. Students cut out paper "tiles" and place these tiles one by one on a larger paper surface called "Launchpad" and use the PlutoAR mobile application which runs on any Android device with a camera and uses augmented reality to output each step of the program like an interpreter. PlutoAR has inbuilt AR experiences like stories, maze solving using conditional loops, simple elementary mathematics and the intuition of gravity.
Visual Transfer between Atari Games using Competitive Reinforcement Learning
Venue: arXiv cs.CV 2018
Year: 2018

This paper explores the use of deep reinforcement learning agents to transfer knowledge from one environment to another. More specifically, the method takes advantage of asynchronous advantage actor critic (A3C) architecture to generalize a target game using an agent trained on a source game in Atari. Instead of fine-tuning a pre-trained model for the target game, we propose a learning approach to update the model using multiple agents trained in parallel with different representations of the target game. Visual mapping between video sequences of transfer pairs is used to derive new representations of the target game; training on these visual representations of the target game improves model updates in terms of performance, data efficiency and stability. In order to demonstrate the functionality of the architecture, Atari games Pong-v0 and Breakout-v0 are being used from the OpenAI gym environment; as the source and target environment.
Effective Exploration for Deep Reinforcement Learning via Bootstrapped Q-Ensembles under Tsallis Entropy Regularization
Venue: arXiv cs.LG 2018
Year: 2018

Recently deep reinforcement learning (DRL) has achieved outstanding success on solving many difficult and large-scale RL problems. However the high sample cost required for effective learning often makes DRL unaffordable in resource-limited applications. With the aim of improving sample efficiency and learning performance, we will develop a new DRL algorithm in this paper that seamless integrates entropy-induced and bootstrap-induced techniques for efficient and deep exploration of the learning environment. Specifically, a general form of Tsallis entropy regularizer will be utilized to drive entropy-induced exploration based on efficient approximation of optimal action-selection policies. Different from many existing works that rely on action dithering strategies for exploration, our algorithm is efficient in exploring actions with clear exploration value. Meanwhile, by employing an ensemble of Q-networks under varied Tsallis entropy regularization, the diversity of the ensemble can be further enhanced to enable effective bootstrap-induced exploration. Experiments on Atari game playing tasks clearly demonstrate that our new algorithm can achieve more efficient and effective exploration for DRL, in comparison to recently proposed exploration methods including Bootstrapped Deep Q-Network and UCB Q-Ensemble.
Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using Semi-supervised Learning
Venue: arXiv cs.RO 2018
Year: 2018

This work studies the semantic segmentation of 3D LiDAR data in dynamic scenes for autonomous driving applications. A system of semantic segmentation using 3D LiDAR data, including range image segmentation, sample generation, inter-frame data association, track-level annotation and semi-supervised learning, is developed. To reduce the considerable requirement of fine annotations, a CNN-based classifier is trained by considering both supervised samples with manually labeled object classes and pairwise constraints, where a data sample is composed of a segment as the foreground and neighborhood points as the background. A special loss function is designed to account for both annotations and constraints, where the constraint data are encouraged to be assigned to the same semantic class. A dataset containing 1838 frames of LiDAR data, 39934 pairwise constraints and 57927 human annotations is developed. The performance of the method is examined extensively. Qualitative and quantitative experiments show that the combination of a few annotations and large amount of constraint data significantly enhances the effectiveness and scene adaptability, resulting in greater than 10% improvement
Delocalisation of one-dimensional marginals of product measures and the capacity of LTI discrete channels
Venue: arXiv cs.IT 2018
Year: 2018

We consider discrete linear time invariant (LTI) channels satisfying the phase independence (PI) assumption. We show that under the PI assumption the capacity of LTI channels is positive. The main technical tool that we use to establish the positivity of the capacity is the delocalisation theorem for one-dimensional marginals of the product measure due to Ball and Nazarov. We also prove two delocalisation results that can be seen as extensions of Ball-Nazarov Theorem.
Reasoning on Adopting OPC UA for an IoT-Enhanced Smart Energy System from a Security Perspective
Venue: arXiv cs.CR 2018
Year: 2018

Smart Services using Industrial Internet of Things (IIoT) applications are on the rise, but still more often than not, traditional industrial protocols are used to interconnect the entities of the resulting systems. These protocols are mostly not intended for functioning in such a highly interconnected environment and, therefore, often lack even the most fundamental security measures. To address this issue, this paper reasons on the security of a communications protocol, intended for Machine to machine (M2M) communications, namely the Open Platform Communications Unified Architecture (OPC UA) and exemplifies, on a smart energy system, its capability to serve as a secure communications architecture by either itself or in conjunction with traditional protocols.
Smart Ticket Protection: An Architecture for Cyber-Protecting Physical Tickets Using Digitally Signed Random Pattern Markers
Venue: arXiv cs.CR 2018
Year: 2018

In order to counter forgeries of tickets for public transport or mass events, a method to validate them, using printed unique random pattern markers was developed. These markers themselves are unforgeable by their physically random distribution. To assure their authenticity, however, they have to be cryptographically protected and equipped with an environment for successful validation, combining physical and cyber security protection. This paper describes an architecture for cryptographically protecting these markers, which are stored in Aztec codes on physical tickets, in order to assure that only an authorized printer can generate a valid Aztec code of such a pattern, thus providing forge protection in combination with the randomness and uniqueness of the pattern. Nevertheless, the choice of the signature algorithm is heavily constrained by the sizes of the pattern, ticket provider data, metadata and the signature confronted by the data volume the code hold. Therefore, this paper also defines an example for a signature layout for the proposed architecture. This allows for a lightweight ticket validation system that is both physically and cryptographically secured to form a smart solution for mass access verification for both shorter to longer periods at relatively low cost.
Automated Machine Learning Service Composition
Venue: arXiv cs.SE 2018
Year: 2018

Automated service composition as the process of creating new software in an automated fashion has been studied in many different ways over the last decade. However, the impact of automated service composition has been rather small as its utility in real-world applications has not been demonstrated so far. This paper presents \tool, an algorithm for automated service composition applied to the area of machine learning. Empirically, we show that \tool is competitive and sometimes beats algorithms that solve the same task but not benefit of the advantages of a service model. Thereby, we present a real-world example that demonstrates the utility of automated service composition in contrast to non-service oriented solutions in the same area.